scholarly journals Integration and Modularity of Quantitative Trait Locus Effects on Geometric Shape in the Mouse Mandible

Genetics ◽  
2004 ◽  
Vol 166 (4) ◽  
pp. 1909-1921
Author(s):  
Christian Peter Klingenberg ◽  
Larry J Leamy ◽  
James M Cheverud
Keyword(s):  
Quantitative Trait ◽  
Parametric Bootstrap ◽  
Interval Mapping ◽  
Procrustes Analysis ◽  
Bootstrap Test ◽  
Mandible Shape ◽  
Trait Locus ◽  
F2 Generation ◽  
Analysis Interval ◽  
Qtl Effects

Abstract The mouse mandible has long served as a model system for complex morphological structures. Here we use new methodology based on geometric morphometrics to test the hypothesis that the mandible consists of two main modules, the alveolar region and the ascending ramus, and that this modularity is reflected in the effects of quantitative trait loci (QTL). The shape of each mandible was analyzed by the positions of 16 morphological landmarks and these data were analyzed using Procrustes analysis. Interval mapping in the F2 generation from intercrosses of the LG/J and SM/J strains revealed 33 QTL affecting mandible shape. The QTL effects corresponded to a variety of shape changes, but ordination or a parametric bootstrap test of clustering did not reveal any distinct groups of QTL that would affect primarily one module or the other. The correlations of landmark positions between the two modules tended to be lower than the correlations between arbitrary subsets of landmarks, indicating that the modules were relatively independent of each other and confirming the hypothesized location of the boundary between them. While these results are in agreement with the hypothesis of modularity, they also underscore that modularity is a question of the relative degrees to which QTL contribute to different traits, rather than a question of discrete sets of QTL contributing to discrete sets of traits.

scholarly journals Analysis of Quantitative Trait Locus Effects on the Size and Shape of Mandibular Molars in Mice

Genetics ◽  
2002 ◽  
Vol 160 (4) ◽  
pp. 1573-1586 ◽  
Author(s):  
Michael Scott Workman ◽  
Larry J Leamy ◽  
Eric J Routman ◽  
James M Cheverud
Keyword(s):  
Interval Mapping ◽  
Size And Shape ◽  
Mandibular Molar ◽  
Shape Effects ◽  
Mandible Shape ◽  
Components Analysis ◽  
Trait Locus ◽  
F2 Progeny ◽  
Shape Data ◽  
Qtl Effects

AbstractWhile >50 genes have been found to influence the development of teeth in mice, we still know very little about the genetic basis for the adaptive characteristics of teeth, such as size and shape. We applied interval mapping procedures to Procrustes size and shape data obtained from 10 morphological landmarks on the mandibular molar row of the F2 progeny from a cross between the LG/J and SM/J strains of mice. This revealed many more QTL for molar shape (18) than for molar centroid size (3), although levels of dominance effects were comparable among QTL for size and shape. Comparisons of patterns of Procrustes additive and dominance shape effects and ordination of QTL effects by principal components analysis suggested that the effects of the shape QTL were dispersed among the three molars and thus that none of these molars represents a genetically distinct developmental structure. The results of an analysis of co-occurrence of QTL for molar shape, mandible shape, and cranial dimensions in these mice suggested that many of the QTL for molar shape may be the same as those affecting these other sets of characters, although in some cases this could be due to effects of closely linked genes.

scholarly journals An investigation of the power for separating closely linked QTL in experimental populations

2010 ◽  
Vol 92 (4) ◽  
pp. 283-294 ◽  
Author(s):  
CHEN-HUNG KAO ◽  
MIAO-HUI ZENG
Keyword(s):  
Power Analysis ◽  
Statistical Power ◽  
Random Mating ◽  
Interval Mapping ◽  
Mapping Method ◽  
Marker Interval ◽  
Correlation Structures ◽  
Experimental Populations ◽  
Trait Locus ◽  
Qtl Effects

SummaryHu & Xu (2008) developed a statistical method for computing the statistical power for detecting a quantitative trait locus (QTL) located in a marker interval. Their method is based on the regression interval mapping method and allows experimenters to effectively investigate the power for detecting a QTL in a population. This paper continues to work on the power analysis of separating multiple-linked QTLs. We propose simple formulae to calculate the power of separating closely linked QTLs located in marker intervals. The proposed formulae are simple functions of information numbers, variance inflation factors and genetic parameters of a statistical model in a population. Both regression and maximum likelihood interval mappings suitable for detecting QTL in the marker intervals are considered. In addition, the issue of separating linked QTLs in the progeny populations from an F2 subject to further self and/or random mating is also touched upon. One of the primary keys to our approach is to derive the genotypic distributions of three and four loci for evaluating the correlation structures between pairwise unobservable QTLs in the model across populations. The proposed formulae allow us to predict the power of separation when several factors, such as sample sizes, sizes and directions of QTL effects, distances between QTLs, interval sizes and relative QTL positions in the intervals, are considered together at a time in different experimental populations. Numerical justifications and Monte Carlo simulations were provided for confirmation and illustration.

scholarly journals Genomic Locus Modulating IOP in the BXD RI Mouse Strains

2017 ◽  
Author(s):  
Rebecca King ◽  
Ying Li ◽  
Jiaxing Wang ◽  
Felix L. Struebing ◽  
Eldon E. Geisert
Keyword(s):  
Candidate Genes ◽  
Quantitative Trait ◽  
Significant Quantitative Trait Locus ◽  
Interval Mapping ◽  
Mouse Strains ◽  
Genomic Locus ◽  
Quantitative Trait Analysis ◽  
Significant Locus ◽  
Trait Locus ◽  
Different Strains

AbstractPurposeIntraocular pressure (IOP) is the primary risk factor for developing glaucoma. The present study examines genomic contribution to the normal regulation of IOP in the mouse.MethodsThe BXD recombinant inbred (RI) strain set was used to identify genomic loci modulating IOP. We measured the IOP from 532 eyes from 33 different strains. The IOP data will be subjected to conventional quantitative trait analysis using simple and composite interval mapping along with epistatic interactions to define genomic loci modulating normal IOP.ResultsThe analysis defined one significant quantitative trait locus (QTL) on Chr.8 (100 to 106 Mb). The significant locus was further examined to define candidate genes that modulate normal IOP. There are only two good candidate genes within the 6 Mb over the peak, Cdh8 (Cadherin 8) and Cdh11 (Cadherin 11). Expression analysis on gene expression and immunohistochemistry indicate that Cdh11 is the best candidate for modulating the normal levels of IOP.ConclusionsWe have examined the genomic regulation of IOP in the BXD RI strain set and found one significant QTL on Chr. 8. Within this QTL that are two potential candidates for modulating IOP with the most likely gene being Cdh11.

scholarly journals Genetic Mapping with Background Control for Quantitative Trait Locus (QTL) in 8-Parental Pure-Line Populations

2019 ◽  
Vol 110 (7) ◽  
pp. 880-891 ◽  
Author(s):  
Jinhui Shi ◽  
Jiankang Wang ◽  
Luyan Zhang
Keyword(s):  
Quantitative Trait Locus ◽  
Genetic Variation ◽  
Linear Model ◽  
Quantitative Trait ◽  
Genetic Model ◽  
Pure Line ◽  
Interval Mapping ◽  
Unbiased Estimation ◽  
Trait Locus ◽  
Marker Variables

Abstract Multiparental advanced generation intercross (MAGIC) populations provide abundant genetic variation for use in plant genetics and breeding. In this study, we developed a method for quantitative trait locus (QTL) detection in pure-line populations derived from 8-way crosses, based on the principles of inclusive composite interval mapping (ICIM). We considered 8 parents carrying different alleles with different effects. To estimate the 8 genotypic effects, 1-locus genetic model was first built. Then, an orthogonal linear model of phenotypes against marker variables was established to explain genetic effects of the locus. The linear model was estimated by stepwise regression and finally used for phenotype adjustment and background genetic variation control in QTL mapping. Simulation studies using 3 genetic models demonstrated that the proposed method had higher detection power, lower false discovery rate (FDR), and unbiased estimation of QTL locations compared with other methods. Marginal bias was observed in the estimation of QTL effects. An 8-parental recombinant inbred line (RIL) population previously reported in cowpea and analyzed by interval mapping (IM) was reanalyzed by ICIM and genome-wide association mapping implemented in software FarmCPU. The results indicated that ICIM identified more QTLs explaining more phenotypic variation than did IM; ICIM provided more information on the detected QTL than did FarmCPU; and most QTLs identified by IM and FarmCPU were also detected by ICIM.

scholarly journals Inheritance and Identification of a Major Quantitative Trait Locus (QTL) that Confers Resistance to Meloidogyne incognita and a Novel QTL for Plant Height in Sweet Sorghum

2015 ◽  
Vol 105 (12) ◽  
pp. 1522-1528 ◽  
Author(s):  
Karen R. Harris-Shultz ◽  
Richard F. Davis ◽  
Joseph E. Knoll ◽  
William Anderson ◽  
Hongliang Wang
Keyword(s):  
Quantitative Trait Locus ◽  
Resistance Gene ◽  
Meloidogyne Incognita ◽  
Quantitative Trait ◽  
Sweet Sorghum ◽  
Major Quantitative Trait Locus ◽  
Interval Mapping ◽  
Chromosome 3 ◽  
Trait Locus ◽  
Stalk Weight

Southern root-knot nematodes (Meloidogyne incognita) are a pest on many economically important row crop and vegetable species and management relies on chemicals, plant resistance, and cultural practices such as crop rotation. Little is known about the inheritance of resistance to M. incognita or the genomic regions associated with resistance in sorghum (Sorghum bicolor). In this study, an F2 population (n = 130) was developed between the resistant sweet sorghum cultivar ‘Honey Drip’ and the susceptible sweet cultivar ‘Collier’. Each F2 plant was phenotyped for stalk weight, height, juice Brix, root weight, total eggs, and eggs per gram of root. Strong correlations were observed between eggs per gram of root and total eggs, height and stalk weight, and between two measurements of Brix. Genotyping-by-sequencing was used to generate single nucleotide polymorphism markers. The G-Model, single marker analysis, interval mapping, and composite interval mapping were used to identify a major quantitative trait locus (QTL) on chromosome 3 for total eggs and eggs per gram of root. Furthermore, a new QTL for plant height was also discovered on chromosome 3. Simple sequence repeat markers were developed in the total eggs and eggs per gram of root QTL region and the markers flanking the resistance gene are 4.7 and 2.4 cM away. These markers can be utilized to move the southern root-knot nematode resistance gene from Honey Drip to any sorghum line.

scholarly journals Composite Interval Mapping Based on Lattice Design for Error Control May Increase Power of Quantitative Trait Locus Detection

PLoS ONE ◽  
2015 ◽  
Vol 10 (6) ◽  
pp. e0130125 ◽  
Author(s):  
Jianbo He ◽  
Jijie Li ◽  
Zhongwen Huang ◽  
Tuanjie Zhao ◽  
Guangnan Xing ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Error Control ◽  
Quantitative Trait Locus Detection ◽  
Interval Mapping ◽  
Lattice Design ◽  
Trait Locus

Least Squares Interval Mapping of Quantitative Trait Loci Under the Infinitesimal Genetic Model in Outbred Populations

Genetics ◽  
1998 ◽  
Vol 148 (1) ◽  
pp. 495-505 ◽  
Author(s):  
Z Liu ◽  
J C M Dekkers
Keyword(s):  
Quantitative Trait ◽  
Null Hypothesis ◽  
Genetic Variance ◽  
Genetic Model ◽  
Interval Mapping ◽  
Substitution Effects ◽  
Polygenic Model ◽  
Marked Chromosome ◽  
Improvement Programs ◽  
Qtl Effects

Abstract Genetic marker and phenotypic data for a quantitative trait were simulated on 20 paternal half-sib families with 100 progeny to investigate properties of within-family-regression interval mapping of a postulated single quantitative trait locus (QTL) in a marker interval under the infinitesimal genetic model, which has been the basis of the application of quantitative genetics to genetic improvement programs, and to investigate use of the infinitesimal model as null hypothesis in testing for presence of a major QTL. Genetic effects on the marked chromosome were generated based on a major gene model, which simulated a central biallelic QTL, or based on 101 biallelic QTL of equal effect, which approximated the infinitesimal model. The marked chromosome contained 0, 3.3%, 13.3%, or 33.3% of genetic variance and heritability was 0.25 or 0.70. Under the polygenic model with 3.3% of genetic variance on the marked chromosome, which corresponds to the infinitesimal model for the bovine, significant QTL effects were found for individual families. Correlations between estimates of QTL effects and true chromosome substitution effects were 0.29 and 0.47 for heritabilities of 0.25 and 0.70 but up to 0.85 with 33.3% of polygenic variance on the marked chromosome. These results illustrate the potential of marker-assisted selection even under the infinitesimal genetic model. Power of tests for presence of QTL was substantially reduced when the polygenic model with 3.3% of genetic variance on the chromosome was used as a null hypothesis. The ability to determine whether genetic variance on a chromosome was contributed by a single QTL of major effect or a large number of QTL with minor effects, corresponding to the infinitesimal model, was limited.

scholarly journals Bayesian Mapping of Multiple Quantitative Trait Loci From Incomplete Outbred Offspring Data

Genetics ◽  
1999 ◽  
Vol 151 (4) ◽  
pp. 1605-1619 ◽  
Author(s):  
Mikko J Sillanpää ◽  
Elja Arjas
Keyword(s):  
Quantitative Trait ◽  
Simulated Data ◽  
Random Variable ◽  
Interval Mapping ◽  
Marker Locus ◽  
Mapping Method ◽  
Monte Carlo Algorithm ◽  
Data Sets ◽  
Outcrossing Species ◽  
Trait Locus

Abstract A general fine-scale Bayesian quantitative trait locus (QTL) mapping method for outcrossing species is presented. It is suitable for an analysis of complete and incomplete data from experimental designs of F2 families or backcrosses. The amount of genotyping of parents and grandparents is optional, as well as the assumption that the QTL alleles in the crossed lines are fixed. Grandparental origin indicators are used, but without forgetting the original genotype or allelic origin information. The method treats the number of QTL in the analyzed chromosome as a random variable and allows some QTL effects from other chromosomes to be taken into account in a composite interval mapping manner. A block-update of ordered genotypes (haplotypes) of the whole family is sampled once in each marker locus during every round of the Markov Chain Monte Carlo algorithm used in the numerical estimation. As a byproduct, the method gives the posterior distributions for linkage phases in the family and therefore it can also be used as a haplotyping algorithm. The Bayesian method is tested and compared with two frequentist methods using simulated data sets, considering two different parental crosses and three different levels of available parental information. The method is implemented as a software package and is freely available under the name Multimapper/outbred at URL http://www.rni.helsinki.fi/~mjs/.

scholarly journals Genomic Locus Modulating IOP in the BXD RI Mouse Strains

2017 ◽  
Author(s):  
Rebecca King ◽  
Ying Li ◽  
Jiaxing Wang ◽  
Felix L. Struebing ◽  
Eldon E. Geisert
Keyword(s):  
Candidate Genes ◽  
Quantitative Trait ◽  
Significant Quantitative Trait Locus ◽  
Interval Mapping ◽  
Mouse Strains ◽  
Genomic Locus ◽  
Quantitative Trait Analysis ◽  
Significant Locus ◽  
Trait Locus ◽  
Different Strains

AbstractPurposeIntraocular pressure (IOP) is the primary risk factor for developing glaucoma. The present study examines genomic contribution to the normal regulation of IOP in the mouse.MethodsThe BXD recombinant inbred (RI) strain set was used to identify genomic loci modulating IOP. We measured the IOP from 532 eyes from 34 different strains. The IOP data will be subjected to conventional quantitative trait analysis using simple and composite interval mapping along with epistatic interactions to define genomic loci modulating normal IOP.ResultsThe analysis defined one significant quantitative trait locus (QTL) on Chr.8 (100 to 106 Mb). The significant locus was further examined to define candidate genes that modulate normal IOP. There are only two good candidate genes within the 6 Mb over the peak, Cdh8 (Cadherin 8) and Cdh11 (Cadherin 11). Expression analysis on gene expression and immunohistochemistry indicate that Cdh11 is the best candidate for modulating the normal levels of IOP.ConclusionsWe have examined the genomic regulation of IOP in the BXD RI strain set and found one significant QTL on Chr. 8. Within this QTL that are two potential candidates for modulating IOP with the most likely gene being Cdh11.

scholarly journals High resolution of quantitative traits into multiple loci via interval mapping.

Genetics ◽  
1994 ◽  
Vol 136 (4) ◽  
pp. 1447-1455 ◽  
Author(s):  
R C Jansen ◽  
P Stam
Keyword(s):  
Recombinant Inbred Lines ◽  
Interval Mapping ◽  
Maximum Likelihood Estimates ◽  
Model Parameters ◽  
Multiple Loci ◽  
F1 Progeny ◽  
F2 Generation ◽  
F2 Progeny ◽  
General Method ◽  
Qtl Effects

Abstract A very general method is described for multiple linear regression of a quantitative phenotype on genotype [putative quantitative trait loci (QTLs) and markers] in segregating generations obtained from line crosses. The method exploits two features, (a) the use of additional parental and F1 data, which fixes the joint QTL effects and the environmental error, and (b) the use of markers as cofactors, which reduces the genetic background noise. As a result, a significant increase of QTL detection power is achieved in comparison with conventional QTL mapping. The core of the method is the completion of any missing genotypic (QTL and marker) observations, which is embedded in a general and simple expectation maximization (EM) algorithm to obtain maximum likelihood estimates of the model parameters. The method is described in detail for the analysis of an F2 generation. Because of the generality of the approach, it is easily applicable to other generations, such as backcross progenies and recombinant inbred lines. An example is presented in which multiple QTLs for plant height in tomato are mapped in an F2 progeny, using additional data from the parents and their F1 progeny.

Sign in / Sign up

Export Citation Format

Share Document